Fair Machine Learning.
Fairness in machine learning has been a research subject with rapid growth and attention recently. Many different definitions of fairness have been designed to fit different settings, e.g., equality of opportunity and equalized odds [1]}, [2]}, direct and indirect discrimination [3]}, [4]}, [5]}, counterfactual fairness [6]}, [7]}, [8]}, and path-specific counterfactual fairness [9]}.
Related but different from our work include long term fairness (e.g., [10]}), which concerns for how decisions affect the long-term well-being of disadvantaged groups measured in terms of a temporal variable of interest, fair pipeline or multi-stage learning
(e.g., [11]}, [12]}, [13]}, [14]}), which primarily consider the combination of multiple non-adaptive sequential decisions and evaluate fairness at the end of the pipeline, and fair sequential learning (e.g., [15]}), which sequentially considers each individual and makes decision for them.
In [10]}, the authors proposed the study of delayed impact of fair machine learning and introduced a one-step feedback model of decision-making to quantify the long-term impact of classification on different groups in the population.
[17]} developed a metric-free individual fairness and a cooperative contextual bandits (CCB) algorithm. The CCB algorithm utilizes fairness as a reward and attempts to maximize it. It tries to achieve individual fairness unlimited to problem-specific similarity metrics using multiple gradient contextual bandits.
